Open vocabulary speech recognition with flat hybrid models
Maximilian Bisani, Hermann Ney · 2005
Today's speech recognition systems are able to recognize arbitrary sentences over a large but finite vocabulary.However, many important speech recognition tasks feature an open, constantly changing vocabulary.(E.g.broadcast news transcription, translation of political debates, etc. Ideally, a system designed for such open vocabulary tasks would be able to recognize arbitrary, even previously unseen words.To some extent this can be achieved by using sub-lexical language models.We demonstrate that, by using a simple flat hybrid model, we can significantly improve a well-optimized state-ofthe-art speech recognition system over a wide range of out-of-vocabulary rates.